Metadata-Version: 2.1
Name: muller
Version: 0.4.13
Summary: A set of scripts to cluster mutational trajectories into genotypes and cluster genotypes by background
Home-page: https://github.com/cdeitrick/muller_diagrams
Author: chris deitrick
Author-email: chrisdeitrick1@gmail.com
License: MIT
Description: # A set of scripts to cluster mutational trajectories into genotypes and cluster genotypes by background
        ![muller_plot](./example/example.muller.unannotated.png)
        
        # Contents
        -  [General Workflow](#general-workflow)
        -  [Requirements](#requirements)
        -  [Script Options](#script-options)
        -  [Input Parameters](#input-dataset)
        -  [Sample Usage](#sample-usage)
        -  [Output Files](#output)
            -  [Output Tables](#tables)
            -  [Muller Plots](#muller-plots)
            -  [Genotype Plots](#timeseries-plots)
            -  [Lineage Diagram](#lineage-diagrams)
        
        # Installation
        These scripts are available on (pypi)[https://pypi.org/project/muller/] and can be installed with
        ```bash
        pip install muller 
        ```
        
        # Requirements
        The scripts require a few python packages to work. Each of these can be installed using `pip install [package]` or `conda install [package]`.
        - dataclasses (if using a python version below 3.7)
        - loguru
        - matplotlib
        - pandas
        - pygraphviz
        - scipy
        - seaborn
        - xlrd (to read excel files)
        
        If the package `pygraphviz` throws an error during installation, it is usually because it can't find the correct dependancies in the current environment.
        Install the dependancies using 
        ```bash
        sudo apt-get install python-dev graphviz libgraphviz-dev pkg-config
        ```
        or the equivalent package manager on your system.
        
        If `tqdm` is also installed, the scripts will display a progressbar for large datasets.
        
        Additionally, `r` should be installed on your system in order to run the generated rscript file with the packages `ggplot2` and `ggmuller`.
        
        # General Workflow
        
        Flowcharts for each individual step can be found under docs/flowcharts.
        
        ![overview](./docs/flowcharts/0-overview.png)
        
        # Script Options
        
        ## General Options
        	-h, --help                  
                                        Show a help message and exit
            --name                      
                                        Prefix to use when naming the output files. defaults to the dataset filename.
        	-i, --input                
                                        The table of trajectories to cluster. Must be an excel file or csv/tsv file.
                                        The delimiter will be inferred from the file extension.
        	-o,  --output               
                                        The output folder to save the files to.
        	-d, --detection             
                                        The uncertainty to apply when performing
        	                            frequency-based calculations. For
        	                            example, a frequency at a given timepoint
        	                            is considered undetected if it falls
        	                            below 0 + `detection`.
        	--fixed                     
                                        The minimum frequency at which to
        	                            consider a mutation fixed. Defaults to
        	                            1 - `uncertainty`
        	-s, --significant           
                                        [0.15] The frequency at which to consider a genotype
        	                            significantly greater than zero.
        	-f, --frequencies           
                                        [0.10] The frequency cutoff or step to use when sorting genotypes.
        	                            May be a comma-separated string of frequencies, or a set inverval
        	                            to use when generating the frequency breakpoints. This affects
                                        the filtering step and the nesting step.
        	                            For example, a value of 0.15 will use the frequencies 0,.15,.30,.45...
        	--genotypes                 Indicates that the input table contains genotypes rather
        	                            than mutational trajectories.
        	--no-filter                 
                                        Disables the genotype filtering step.
            --sheetname                 
                                        Specifies the sheet to use when the input is an excel file. Defaults to
                                        'Sheet1'
            --strict-filter             
                                        By default, the filters allow trajectories to appear both before and after a genotype
                                        fixes as long as they were undetected at the timepoint the sweep occurs. This generally
                                        represents mutations which appear, are removed during a genotype sweep, and reappear
                                        afterwards. Using `--strict-filter` would remove these trajectories.
            --genotype-colors           Path to a file with a custom genotype colorscheme. The file should be tab-delimited
                                        with a genotype name (ex. 'genotype-13') in the first column and a HEX color code
                                        (ex. '#F5674A') in the second. These colors will override the default colorscheme.
            --gene-alias ALIAS_FILENAME
                                        An optional two-column file with more accurate gene
                                        names. This is useful when using a reference
                                        annotated via prokka.
        
        ## Clustering Options
            -m, --method                Selects the clustering method to use. 'two-step' will use the original two-step
                                        method of sorting trajectories into genotypes while 'hierarchy' will use
                                        hierarchical clustering to do the clustering. Defaults to 'matlab'
            --metric                    Used to select the distance metric when `--method` is set to 'hierarchy'.
                Available Options:
                'similarity', 'binomial' [Default] Uses the binomial test implemented in the original matlab scripts as a distance metric.
                'jaccard'               Uses the Jaccard distance between two series to determine the distance metric.
                'minkowski'             Uses the minkowski distance as a distance metric. Primarily influenced by the
                                        difference between two series.
                'pearson'               Uses the pearson correlation coefficient as the distance metric. Primarily
                                        influenced by the correlation of two series against each other.
                'combined'              A combination of the 'pearson' and 'minkowski' distances to account for the
                                        correlation of two series as well as the difference between them.
            -r --similarity-cutoff      
                                        [0.05] Used when grouping trajectories into genotypes.
                                        Maximum p-value difference to consider trajectories related when using
                                        the two-step method, and selects the maximum distance to consider
                                        trajectories related when `--method` is `hierarchy`.
        
            -d, --difference-cutoff     [0.10] Only used when `--method` is `twostep`.
                                        Used to unlink unrelated trajectories present in a genotype. Is not used
                                        when using hierarchical clustering.
            -g, --known-genotypes       
                                        Path to a file listing trajectories which are known to be in the same genotype.
                                        Each line in the file represents a single genotype, and each line should be a
                                        comma-separated list of trajectory labels as they appear in the input dataset.
        
        ## Nesting Options
            --additive
                                        [0.03] Controls how the additive score between a nested and
                                        unnested genotype is calculated. Defaults to the
                                        detection cutoff value.
            --subtractive
                                        Controls when the combined frequencies of a nested and
                                        unnested genotype are considered consistently larger
                                        than the fixed cutoff.Defaults to the detection cutoff
                                        value. (default: None)
            --derivative
                                        Controls how much a nested and unnested genotype
                                        should be correlated/anticorrelated to be considered
                                        significant (default: 0.01). Correlation implies a positive relationship
                                        between the nested/unnested genotypes while anticorrelation is evidence
                                        against nesting the unnested genotype under the nested genotype.
            --known-ancestry
                                        A tab-delimited file designating the known ancestry of certain
                                        genotypes. The left column should be the genotype to nest,
                                        right column should be its parent. Column names are ignored.
                                        Genotype names are generated during the clustering step,
                                        so this is only useful when re-running the analysis.
        
        ## Graphics Options
            --no-ouline
                                        Disables the white ouline surrounding each series in the muller plots.
        
        # Input Dataset
        
        The script operates on a table listing all mutations and their corresponding frequencies at each timepoint (refered to as "trajectories" in this script) or a table with each genotype and frequency at each timepoint (ex. the genotype table in the examples folder).
        The table must have a column named `Trajectory` with labels for each mutational trajectory (or `Genotype` when using `--genotype`) and integer columns for each timepoint. The labels are solely used to identify trajectories belonging to a specific genotype, and must be integers. All other columns will be ignored when calculating genotypes and genotype clusters.
        The frequencies can be represented as either a number between 0 - 1,
        a number between 0 - 100 or as percentage.
        The `Trajectory` and `Genotype` columns can contain any kind of label, but must be unique for each trajectory/genotype.
        
        | Population | Trajectory    | Chromosome | Position | Class | Mutation | 0 | 17    | 25    | 44    | 66    | 75    | 90    |
        |------------|---------------|------------|----------|-------|----------|---|-------|-------|-------|-------|-------|-------|
        | B2         | 1             | 1          | 38102    | SNP   | C>T      | 0 | 0     | 26.1% | 100%  | 100%  | 100%  | 100%  |
        | B2         | 2             | 1          | 62997    | SNP   | T>G      | 0 | 0     | 0     | 52.5% | 45.4% | 91.1% | 91%   |
        | B2         | 3             | 1          | 78671    | SNP   | A>C      | 0 | 0     | 0     | 14.7% | 45%   | 92.4% | 88.7% |
        | B2         | 4             | 1          | 96585    | SNP   | T>G      | 0 | 0     | 0     | 0     | 21.1% | 81.1% | 81.3% |
        | B2         | 5             | 1          | 115010   | SNP   | G>T      | 0 | 0     | 0     | 40.3% | 48.9% | 5.7%  | 8%    |
        | B2         | t16           | 1          | 299332   | SNP   | C>T      | 0 | 0     | 0     | 0     | 20.9% | 20.9% | 0     |
        | B2         | 6             | 1          | 156783   | SNP   | C>G      | 0 | 0     | 0     | 0     | 0     | 100%  | 100%  |
        | B2         | 7             | 1          | 176231   | SNP   | T>A      | 0 | 0     | 0     | 27.3% | 78.1% | 100%  | 100%  |
        | B2         | 8             | 1          | 205211   | SNP   | C>T      | 0 | 0     | 0     | 0     | 34.5% | 83.3% | 79.3% |
        | B2         | 9             | 1          | 223199   | SNP   | C>G      | 0 | 0     | 0     | 0     | 0     | 26.9% | 34%   |
        | B2         | trajectory-10 | 1          | 262747   | SNP   | T>C      | 0 | 0     | 11.7% | 0     | 0     | 0     | 10.3% |
        | B2         | trajectory-11 | 1          | 264821   | SNP   | C>T      | 0 | 0     | 0     | 10.8% | 15.1% | 0     | 0     |
        | B2         | trajectory-12 | 1          | 298548   | SNP   | G>A      | 0 | 12.5% | 0     | 15.3% | 18.1% | 17.5% | 19.1% |
        | B2         | trajectory-13 | 1          | 299331   | SNP   | G>A      | 0 | 0     | 0     | 0     | 25.8% | 5.7%  | 7.5%  |
        | B2         | trajectory-14 | 1          | 299332   | SNP   | C>T      | 0 | 38%   | 43.2% | 0     | 0     | 0     | 0     |
        | B2         | t15           | 1          | 299332   | SNP   | C>T      | 0 | 0     | 6.6%  | 10.4% | 6.2%  | 0     | 0     |
        | B2         | t16           | 1          | 299332   | SNP   | C>T      | 0 | 0     | 0     | 0     | 20.9% | 20.9% | 0     |
        | B2         | t17           | 1          | 299332   | SNP   | C>T      | 0 | 0     | 0     | 0     | 0     | 26.6% | 31.2% |
        | B2         | t18           | 1          | 299332   | SNP   | C>T      | 0 | 0     | 0     | 11.5% | 0     | 13.1% | 0     |
        | B2         | t19           | 1          | 299332   | SNP   | C>T      | 0 | 0     | 0     | 18.8% | 17.1% | 23.2% | 24.4% |
        | B2         | 20            | 1          | 299332   | SNP   | C>T      | 0 | 0     | 0     | 13.8% | 29.5% | 0     | 8.1%  |
        | B2         | 21            | 1          | 299332   | SNP   | C>T      | 0 | 0     | 0     | 11.4% | 0     | 11%   | 12.3% |
        
        # Sample Usage
        The scripts currently default to hierarchical clustering using the binomial distance. More information is available in the "description" folder.
        Use python to call the "muller" folder:
        ```
        python muller --input [input filename] --output [output folder]
        ```
        
        Run with default parameters.
        
        ```
        python muller --input [filename] --frequencies 0.05 --detected 0.10
        ```
        Groups genotypes in groups of 0.05 (i.e. `[0.00, 0.05, 0.10, ... , 0.90, 0.95, 1.00]`) based on each genotype's maximum frequency. Each genotype in each group is then sorted by the timepoint it was first detected (the first timepoint where the frequency was greater than 0.10). Output files are saved to the same folder as the input table.
        
        # Output
        All files are prefixed by the name of the original input table if the `--name` parameter is unfilled.
        
        ## Tables
        
        ### Timeseries tables
        - .muller_genotypes.tsv
        - .muller.trajectories.tsv
        - tables/.muller_genotypes.original.tsv
        - tables/.trajectories.original.tsv
        
        Tables listing the genotypes and trajectories encountered in the analysis. The trajectory tables also link each trajectory to its respective genotype. There are two versions of these tables: one set with the original input trajectories and the initial calculated genotypes and another set with the final trajectories and genotypes left in the analysis after the filtering step. The trajectory tables include all columns from the input trajectory table as well as the timeseries and annotation columns used in the analysis.
        
        Example Genotype Table:
        
        | Genotype    | 0.000 | 17.000 | 25.000 | 44.000 | 66.000 | 75.000 | 90.000 |
        | ----------- | ----- | ------ | ------ | ------ | ------ | ------ | ------ |
        | genotype-1  | 0.000 | 0.380  | 0.432  | 0.000  | 0.000  | 0.000  | 0.000  |
        | genotype-2  | 0.000 | 0.000  | 0.000  | 0.403  | 0.489  | 0.057  | 0.080  |
        | genotype-3  | 0.000 | 0.000  | 0.000  | 0.000  | 0.000  | 1.000  | 1.000  |
        | genotype-4  | 0.000 | 0.000  | 0.261  | 1.000  | 1.000  | 1.000  | 1.000  |
        | genotype-5  | 0.000 | 0.000  | 0.000  | 0.273  | 0.781  | 1.000  | 1.000  |
        | genotype-6  | 0.000 | 0.000  | 0.092  | 0.052  | 0.031  | 0.000  | 0.052  |
        | genotype-7  | 0.000 | 0.000  | 0.000  | 0.000  | 0.278  | 0.822  | 0.803  |
        | genotype-8  | 0.000 | 0.000  | 0.000  | 0.336  | 0.452  | 0.918  | 0.899  |
        | genotype-9  | 0.000 | 0.000  | 0.000  | 0.076  | 0.043  | 0.219  | 0.255  |
        | genotype-10 | 0.000 | 0.021  | 0.000  | 0.086  | 0.182  | 0.095  | 0.058  |
        
        
        ### Tables for ggmuller
        - tables/.ggmuller.populations.tsv
        - tables/.ggmuller.edges.tsv
        
        These tables are designed for use with the ggmuller r package. The `populations` table describes the population/abundance of each genotype at each timepoint while the `edges` table describes the ancestry relationship between genotypes.
        
        ### Linkage matrix
        - tables/.linkagematrix.tsv
        
        This table is generated using the [scipy](https://docs.scipy.org/doc/scipy/reference/cluster.hierarchy.html) python package. It describes the agglomeration of clusters starting with the individual trajectories, as well as the mean, variance, and trajectory count of each cluster.
        Columns:
        - `left`, `right`: The two sub-clusters merged to create the current clusters
        - `clusterId`: The id assigned to this cluster. Note that since the individual genotypes are not included in the table, the clusters are numbered in order starting with 1 + the total number of genotypes.
        - `distance`: The distance between the two sub-clusters.
        - `observations`: The number of mutational trajectories contained in this cluster.
        
        Example linkage matrix:
        
        | left | right | distance | observations | resultingCluster |
        |------|-------|----------|--------------|------------------|
        | 7    | 18    | 0.034    | 2            | 19               |
        | 13   | 17    | 0.175    | 2            | 20               |
        | 8    | 11    | 0.199    | 2            | 21               |
        | 2    | 5     | 0.239    | 2            | 22               |
        | 10   | 3     | 0.279    | 2            | 23               |
        | 9    | 12    | 0.370    | 2            | 24               |
        | 23   | 6     | 0.529    | 3            | 25               |
        | 22   | 21    | 0.624    | 4            | 26               |
        | 26   | 1     | 0.708    | 5            | 27               |
        | 24   | 16    | 0.760    | 3            | 28               |
        | 14   | 25    | 0.786    | 4            | 29               |
        | 15   | 20    | 0.988    | 3            | 30               |
        | 29   | 27    | 1.094    | 9            | 31               |
        | 31   | 19    | 1.358    | 11           | 32               |
        | 30   | 28    | 1.362    | 6            | 33               |
        | 4    | 32    | 1.499    | 12           | 34               |
        | 33   | 0     | 2.125    | 7            | 35               |
        | 34   | 35    | 4.943    | 19           | 36               |
        
        ### Distance Matrix
        - tables/.distance.tsv
        
        A table of pairwise distance values between each trajectory.
        
        ### Muller table
        - tables/.muller.tsv
        
        The converted form of the `.ggmuller.populations.tsv` and `.ggmuller.edges.tsv` used to generate the muller plots. This file is created from the r script, described later.
        
        ## Graphics
        Each of the output plots use the same palette for genotypes and trajectories. A genotype colored a shade of blue will share that color across all graphs and diagrams which depict that genotype. There are two palettes: one to indicate each clade in the geneology and one to easily distinguish between different genotypes. Each graphic is created with both palettes, and some are provided in multiple formats for convienience.
        
        ### Muller Plots
        - .muller.annotated.png
        - graphics/clade/.muller.annotated.svg
        - graphics/clade/.muller.annotated.png
        - graphics/clade/.muller.unannotated.png
        - graphics/distinctive/.muller.annotated.distinctive.png
        - graphics/distinctive/.muller.annotated.distinctive.svg
        
        The main value of a muller plot is to quickly visualize abundance and geneology of genotypes over the course of an evolution experiment.
        
        ![muller](example/example.muller.annotated.png)
        
        ### Lineage Diagrams
        - .lineage.png
        - graphics/.lineage.distinctive.png
        
        These are simple flowcharts indicating the relationship between genotypes and clades. The original genotype of each clade are shown to arise in "genotype-0", the root background. The ancestry of all other genotypes are then shown relative to these clades.
        
        ![geneology](example/example.lineage.png)
        
        ### Timeseries plots
        - .genotypes.png
        - .genotypes.filtered.png
        - .trajectories.distinctive.png
        
        Timeseries plots of the frequency of each trajectory and genotype at each timepoint. Trajectories are colored according to which genotype they were grouped into. The `.genotypes.filtered.png` file includes trajectories that were filtered out during the filtering step (clored black).
        
        ![timeseries](example/graphics/distinctive/example.genotypes.distinctive.png)
        
        ### Distance Heatmap
        - graphics/.heatmap.distance.png
        
        A pairwise comparison of the calculated distance between each mutational trajectory. Trajectories are grouped by the final genotype. The heatmap will be annotated with the distance values if there are fewer than thirty total trajectories in the analysis.
        
        ![heatmap](example/graphics/example.heatmap.distance.png)
        
        ### Dendrogram
        - graphics/.dendrogram.png
        Shows the arrangement and distance between clusters and member trajectories. Not available with `--method twostep`.
        
        ![dendrogram](example/graphics/example.dendrogram.png)
        
        ## Scripts
        - scripts/example.r
        
        One external script is used during the course of this analysis. The r script is based on the [ggmuller](https://cran.r-project.org/web/packages/ggmuller/vignettes/ggmuller.html) package implemented in r, and is used to convert the genotypes data into a format required to generate the muller plots. This script also generates a basic muller plot (/graphics/distinctive/.muller.png), although all other muller plots are created with the python implementation.
        
        ## Supplementary files
        - supplementary-files/.json
        
        A json-formatted file with all parameters used in the analysis.
        
        - supplementary-files/.nestscores.tsv
        
        Lists the scores between each genotype and the corresponding candidate ancestry genotypes. The highest score above or equal to 1 determines the parent genotype.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Provides: m
Provides: u
Provides: l
Provides: l
Provides: e
Provides: r
Description-Content-Type: text/markdown
